Research on safe path planning for unmanned aerial vehicles based on an Improved Pied Kingfisher Optimization Algorithm

The safety route planning problem in UAVs (Unmanned Aerial Vehicles)missions is the subject of this paper, which suggests a method based on an improved Pied Kingfisher Optimization algorithm (IPKO) algorithm. By adding collision avoidance restrictions to the swarm scenario, the study expands the single UAVs path planning problem to improve flight safety. In order to reduce the problem of decreased convergence speed in the PKO algorithm, population diversity is increased and premature convergence to the local optimum is prevented by using mirror reflection learning. Furthermore, the algorithm is improved with a crash mechanism model and a fish hawk hunting approach, which allows UAVs to react in real time to environmental changes and avoid hazards. The IPKO algorithm performs better than conventional algorithms in terms of path efficiency and safety, as evidenced by experimental results, which also provide a fresh view of UAV safety path planning.